The simple random sampling is one of the most widely-used random sampling method. The term “random” here does not mean a haphazard selection as many people think. The “random” in this method means each member of the population has equal opportunities being chosen be subject and no one in the identified population who could not be selected in this method. For example, the teacher wants to choose 5 people in QTB class to stand up and introduce themselves. In order to perform random sampling, each member has to have a specific number as an ID, and those number are put in a random sample list or termed sampling frame. In the example, sampling frame would be the class list. The mechanical and primitive method would be the lottery method. Each number is placed in a bowl or a container and mixed thoroughly. After that, the researcher picks numbers tags from the container without any awareness of what numbers that are. All the individuals bearing the numbers picks are the subjects for the study. Thank to advance technology; another way to perform this sampling would be using computer or calculator to do a random selection from the population.
There are two types: sampling without replacement and with replacement in simple random sampling. In the first example about choosing students to introduce themselves, the student who have talk about themselves could not be chosen one more time. So the teachers need to remove their number out of the sampling frame. When we look at another example like lottery, the numbers are picked, and then they are put back to the container. Those numbers, which are put back, may be selected more than once. Simple Random sampling has its advantages and disadvantages. On one hand, the best thing about this random sampling is that it is easy to perform. Moreover it is also considered as an unbiased random selection since every member is given equal chances of being selected. On the other hand, there is the most obvious limitation of simple random sampling method is its sampling frame required. The sampling frame must be complete and up to date, which is not usually available for large population.
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stratified random sampling:
Stratified random sampling, which is a variant of probability sampling technique, is used when population may have different value for the responses of interest. The researcher wants to highlight particular subgroups in the whole population. In this case, unlike the simple random sampling, we divide the population into groups that are called strata, than randomly selects the final subjects from the different strata. Each individual or unit in a stratum has same opportunity to be chosen. In order to give equal chance to each unit, the researcher must apply the simple random sampling within the different strata and more important is that the strata must be non-overlapping. Having overlapping means that some units will have higher chance to be chosen as subject. For example, to choose students introducing themselves at BA1, the teacher would first organize the class into groups like Asian, European, American and so on. After dividing students into groups, the teacher chooses randomly students from each group. By doing that, the teacher certainly does not miss any continent, which could happen when the teacher just used the simple random sampling. There are two types of stratified random sampling: Proportionate and disproportionate stratified random sampling. In proportionate stratified random sampling, each stratum has the same sampling fraction.
... agenda for the group to follow. Students will usually work in groups such as collaborative groups, performance based groups, and student pairs. Collaborative groups consist of a ... . There are two common regrouping strategies: teacher-led groups and student-led groups. Teacher-led groups are effective in introducing material, summing up the ...
Take dividing groups to introduce themselves as an example, the teacher chooses a sampling fraction of a half, this means a half of students each group are selected to introduce themselves. In disproportionate stratified random sampling, there are different sampling fractions in each group. For example, the teacher needs a half of European students and a quarter of Asian students because of the number of Asian students stand over the European one. Stratified random sampling has its advantages and disadvantages. A stratified sample gives greater precision than the simple random sampling of the same population, which make stratified random sampling be better to use a small group to save time and money. Another advantage of stratified random sampling is highly representative of the population being studied. However, like simple random sampling, stratified random sampling requires a sampling frame that must be completed and list of each stratum. The sampling frame also needs to be divided into strata. Then data analysis should take sampling “weight” into account for disproportionate sampling of strata.
convenience sampling is a non-random sampling method where subjects are chosen because of their convenient accessibility to the researcher. This method could be used in any field of research, including, political science, psychology, sociology and biological fields of study, when researcher attempt to assess trends of public or to gain a better understanding of changes in biology. For example, one student wants to know about how popular of the new type of music. That student could make a survey in class within his friends, because it is quite easy for that student ask his classmate. Convenience sampling has many advantages for researching. The most realizable benefit is its fast, inexpensive, easy and all the subjects are readily available.
Moreover, convenience sampling enables researcher to gather data even when facing obstacles. For instant, businesses may not be able to give out some specific information on their employees, or their stores. By this general sampling, researchers could catch the basic information. The limitation of convenience sampling comes belong its benefits as well. This sample is not representative of the entire population. Therefore it would be the restriction in generalization and inference making about the whole population. Though it is not the most accurate methods, it is one of the most used forms of sampling. Without convenience sampling, many research projects would never be completed.
... simplify the population. The multi-stage form of sampling is flexible in many senses. First, it allows researchers to employ random sampling or cluster sampling after ... fairly simple, since our strata are male and female students. Clearly, a student could only be classified as either male or female ...
In this sample, the researcher or some other expert uses his or her judgment in choosing the subjects from the population. It means there is no element of chance and judgment is used to select participant. For example, the teacher looks around and chooses some specific students in class BA 1 to introduce themselves. Judgment sampling is usually used when a limited number of individuals possess the trait of interest and the researchers want to use local knowledge. In many cases, the judgment sampling is used for illustrative purposes rather than statistical inference to the general population. There are two main weaknesses of judgment sampling are with the authority and in the sampling process; both of which pertains to the reliability and the bias that accompanies the sampling technique. The good way to reduce sampling error is to choose the best and most experienced researcher or expert.
Snowball sampling is a non-random sampling technique which is used be researchers when possible respondents are difficult to identify and often, relatively rare. Snowball sampling uses a small survey to nominate, and through their social network, the research would become widespread. The term snowball sample refers to the expanding of the research as the snowball when it rolls down the hill. There are some types of snowball sampling:
* Linear Snowball sampling
* Exponential Non-Discriminative Snowball Sampling
* Exponential Discriminative Snowball Sampling
On one hand, there are some advantages of snowball sampling. The chain referral process enables the researcher to reach populations that are difficult to survey. Moreover, this sampling is cheap, simple and needs less planning, fewer workforces than other sampling method. On the other hand, Snowball sampling also have some disadvantages. The researchers who use this sampling has less control over the survey, the first subject have influence on the next subjects of the research. Further more, the representativeness of the sample in not guaranteed.
... sample (Monette, Sullivan, & DeJong, 2011). There are also five methods of nonprobability which are availability sampling, snowball sampling, quota sampling, purposive sampling, and dimensional sampling ... research can be conducted on individuals. If a researcher wants to know about a veteran who suffers ... of internet surveys is not many people would choose to take the time to complete an online ...